For each dataset, we randomly split the set of nodes into training/validation/test sets with a ratio of 85/5/10%. The parameter \(\alpha \)  in (REF ) for designing our topology-aware ranking strategy and the threshold \(\epsilon \)  in the reinforced weighted graph generation are set to 1 and 0.5, respectively, unless otherwise stated, where the impact of \(\alpha \)  and \(\epsilon \)  on the performance will be empirically examined in Section REF . For the function ModifiedGreedy, we employ the implementation of the CELF algorithm [1]}, in which the number of Monte-Carlo simulations is set to 20,000.
